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Over the past 50 years, geographers have embraced each new technological shift in geographic information systems (GIS)—the technology that turns location data into maps and insights about how places and people interact—first the computer boom, then the rise of the internet and data-sharing capabilities with web-based GIS, and later the emergence of smartphone data and cloud-based GIS systems.

Now, another paradigm shift is transforming the field: the advent of artificial intelligence (AI) as an independent "agent" capable of performing GIS functions with minimal human oversight.

In a study in Annals of GIS, a multi-institutional team led by geography researchers at Penn State built and tested four AI agents in order to introduce a conceptual framework of autonomous GIS and examine how this shift is redefining the practice of GIS.

"Just like the paradigm shifts of the past, autonomous GIS represents an emerging paradigm of integrating AI with GIS, where it is not just another tool but instead becomes an artificial geospatial analyst able to use GIS tools to solve geospatial problems," said lead author Zhenlong Li, associate professor of geography in the College of Earth and Mineral Sciences and director of the Geoinformation and Big Data Research Lab.

"In this study, we spent time exploring, as a GIS community, how to best integrate AI 'agent' technology into existing GIS workflows, and analyzing the current drawbacks and limitations of the systems," Li said. "Our goal is to lay the groundwork for the geospatial community to develop GIS systems that move beyond traditional workflows to autonomously reason, derive, innovate and advance geospatial solutions to pressing challenges."

With their four proof-of-concept AI-powered GIS agents, researchers demonstrated that the agents can retrieve geospatial data, perform spatial analysis and generate maps with minimal human intervention.

In one case study, the researchers built a data retrieval agent called LLM-Find, first in the International Journal of Digital Earth, which automatically fetches geospatial datasets based on users' requests, such as "download road networks excluding footways and service ways for a school walkability assessment in Columbia, South Carolina." Within minutes, LLM-Find obtained data on sidewalks, road networks, school locations and high-resolution remote-sensing imagery needed for a complex assessment.

"LLM-Find demonstrated that autonomous GIS agents can handle data acquisition from sources without manual dataset hunting, helping to reduce the grunt work of data preparation in spatial analyses," Li said. "But the number of sources the AI agent can consult is still limited, so human oversight and management is needed for LLM-Find."

The next GIS agent that researchers built and tested, LLM-Geo, assessed school walkability using the data fetched by LLM-Find, then autonomously generated a spatial workflow that produced walkability scores and maps.

"This is a more complex task that goes beyond data retrieval, where the AI agent is actually doing analysis of data based on a plain-language prompt," Li explained. "This analysis work might normally be done by a junior or entry-level geographer."

The next case study, LLM-Cat, completed more rigorous cartographic tasks—going beyond data acquisition and analysis to design visual maps. The agent made decisions on symbols, color scales, map views and other map elements, bringing the whole system closer to full automation.

The final case study brought together all three agents into a collaborative desktop human-to-agent assistant, , which functions similar to ChatGPT or Google Gemini.

"We tested GIS Copilot across over 100 multi-step guided spatial tasks and advanced unguided tasks with an overall success rate of about 86%," Li said. "Though it still needs human supervision, GIS Copilot shows what is possible for future GIS integration with AI, particularly in allowing non-experts to perform geospatial analysis with limited knowledge of the field."

Integrating AI in GIS is opening new horizons for GIS scientists and experts, according to co-author Guido Cervone, director of the Penn State Institute for Computational and Data Sciences. He explained that rather than a threat to the livelihood of professionals, it provides multiple opportunities to grow GIS and apply it in innovative ways.

"In the last five years, we have seen more progress in GIS than I thought I was going to see in my lifetime," said Cervone, who also is a professor in the Departments of Geography and of Meteorology and Atmospheric Science. "From a research perspective, AI accelerated our research impact to new levels, as we can now quickly access and analyze data to better study our planet and its natural and built environment."

Looking toward the future, Cervone highlighted the changes that will take place in education, noting that it's an exciting time to be a student or a professor in geography and geoinformatics.

"If the recent past is an example, then we will have surprises and be able to achieve tasks that are impossible to even imagine today," Cervone said. "This will happen within a generation of our students, and our challenge as professors is to train them to be part of the AI and computing revolution and prepare them for new challenges and innovative solutions."

Li agreed, explaining that the study is not only about advancing the technology of AI, but also about the changes coming for the next generation of geography students.

"It's now more important for students to understand process or spatial thinking procedures, to learn how to learn in the age of AI in GIS," Li said. "It's not only important for students, but as educators for us to be aware of what might change in the classroom and in the workforce in the GIS space."